how to import seaborn in python jupyter notebook

isnull() built-in function converts the column values into boolean True and False values and returns them in a new dataframe. Fortunately, there’s a handy way that we can use to check for nulls: When we start building models with our data, null values in observations are almost never allowed. Checking Null Valuesĭetermining how many values are missing for each feature column is an important step in validating our data. You can specify the number of rows you wish to see by typing a number in the parentheses. We can see the first five rows by inputting ‘df.head()’. Let’s load the data and name the variable ‘df’. This allows us to store the plot within the notebook. Life’s too short for pixelated graphs, right? Finally, ‘%matplotlib inline’ is a magic functionthat tells Matplotlib to generate our plots within the frontend of the Jupyter Notebook under our code.

The ‘sns.set_style’ sets the aesthetics of the plot and ‘%config InlineBackend.figure_format = ‘retina’’ makes the plot higher resolution. These abbreviations are accepted as industry standard but you can name it to whatever you wish. As you can see, I have renamed the libraries using the ‘as’ statement. Let’s start by importing libraries we will need to conduct some basic EDA. Please note that this example is purely an example and I did not form any hypotheses surrounding this data which could influence my focus and choices in what to investigate.

In this article, we will use both libraries to conduct an example EDA on Jupyter Notebook using the Boston Housing dataset from Kaggle. Both are usually used in conjunction during the EDA process because Seaborn’s default color themes are better than Matplotlib but Matplotlib is easier to customize. Seaborn is another commonly used library for data visualization and it is based on Matplotlib. Matplotlib is a data visualization library that can create static, animated, and interactive plots in Jupyter Notebook. Python is a great language for data science because it has two libraries called Matplotlib and Seaborn that will help you visualize data. However, conducting a thorough EDA is important to get a better sense of what your data looks like and ensure there are no outliers or missing values that might skew your analysis. It is tempting to train models right away and see the results to make decisions. Learn data visualization in Python using Matplotlib and Seaborn in this data visualization guide.Įxploratory data analysis (EDA) is often overlooked in data science projects.

How to import seaborn in python jupyter notebook